IEEE Annual Congress on Artificial Intelligence of Things (IEEE AIoT)

14-16 Dec 2026 · Kobe, Japan.

http://www.ieee-aiot.org/2026

Track 2: AI for IoT Communications and Networking

Track Chairs:
Berk Canberk, Edinburgh Napier University, UK, b.canberk@napier.ac.uk
Hossein Fotouhi, Mälardalen University, Sweden, hossein.fotouhi@mdu.se
Jehad Ali, Ajou University, South Korea, jehadali@ajou.ac.kr

Description

The convergence of IoT, edge-cloud computing, digital twinning and emerging 6G infrastructures is accelerating the evolution toward AI-native communication systems capable of autonomous operation, intelligent orchestration, and context-aware adaptation. Future IoT networks are expected to support massive scalability, ultra-low latency, cyber resilience, energy efficiency, and distributed intelligence across highly heterogeneous environments. Recent advances in machine learning, generative AI, foundation models, federated learning, and multi-agent intelligence are enabling transformative approaches to network automation, resource management, semantic communications, intelligent edge services, and autonomous network optimization. These technologies are reshaping how IoT communication systems are designed, deployed, secured, and operated. This track aims to bring together researchers, practitioners, and industry experts working on AI-enabled and AI-native IoT communications, networking, and 6G systems. The track welcomes original research papers, experimental studies, testbed implementations, industrial experiences, and visionary contributions addressing theoretical advances and real-world challenges in intelligent IoT networking.

Track Topics

• AI-native communication architectures for IoT and 6G
• AI-driven network slicing and orchestration
• Quantum-enabled Digital Twin frameworks for future IoT systems
• Real-time Digital Twin architectures for IoT and 6G networks
• Semantic communications for intelligent IoT systems
• Integrated sensing and communication (ISAC) for IoT
• AI-assisted mobility, handover, and topology management
• Intelligent access selection and connectivity management
• Multi-agent Digital Twin intelligence and coordination
• AI-enabled ultra-reliable and low-latency communications (URLLC)
• AI for heterogeneous and interoperable IoT networks
• Edge AI and distributed intelligence for IoT
• Federated and collaborative learning for IoT networking
• TinyML for resource-constrained IoT systems
• AI-assisted cloud-edge-IoT resource orchestration
• Distributed AI for large-scale IoT environments
• AI-enabled data offloading and task scheduling
• Reinforcement learning for autonomous network optimization
• AI-driven routing and congestion control
• AI-enabled self-organizing and self-healing networks
• Generative AI and LLMs for network automation
• Multi-agent AI for IoT communications
• AI-based QoS/QoE optimization
• AI-assisted adaptive MAC and radio resource management
• AI for cybersecurity and cyber resilience in IoT networks
• Trustworthy, explainable, and privacy-preserving AI
• AI-enabled intrusion and anomaly detection
• AI-based energy-efficient and green IoT networking
• Secure federated learning and distributed AI systems
• AI-enabled industrial IoT and private 5G/6G systems
• AI for vehicular IoT and V2X communications
• AI-enabled UAV and aerial IoT networking
• AI-driven smart city communication systems
• Digital twins for IoT communications and networking
• Experimental testbeds, real-world deployments, and industrial experiences

Paper Submission and Publication

Details of paper submission and publication can be found here.